3）翻译句子

th translate.lua -model model_final.t7 -src data/src-test.txt -output pred.txt

查看指南了解更多：http://opennmt.github.io/Guide

研究

　　其中主要的模型基于论文 Neural Machine Translation by Jointly Learning to Align and Translate Bahdanau et al. ICLR 2015 和 Effective Approaches to Attention-based Neural Machine Translation, Luong et al. EMNLP 2015。

　　在基本模型上，还有大量可选项，这都要感谢 SYSTRAN（http://www.systransoft.com/）的出色工作。特别地，下面是

一些实现的功能：

Effective Approaches to Attention-based Neural Machine Translation . Luong et al., EMNLP 2015.

Character-based Neural Machine Translation. Costa-Jussa and Fonollosa, ACL 2016.

Compression of Neural Machine Translation Models via Pruning . See et al., COLING 2016.

Sequence-Level Knowledge Distillation . Kim and Rush., EMNLP 2016.

Deep Recurrent Models with Fast Forward Connections for Neural Machine Translation . Zhou et al, TACL 2016.

Guided Alignment Training for Topic-Aware Neural Machine Translation . Chen et al., arXiv:1607.01628.

Linguistic Input Features Improve Neural Machine Translation . Senrich et al., arXiv:1606.02892

声明

　　OpenNMT 的实现使用了以下项目的代码：

Andrej Karpathy 的 char-rnn：https://github.com/karpathy/char-rnn

Wojciech Zaremba 的 LSTM：https://github.com/wojzaremba/lstm

Element RNN 库：https://github.com/Element-Research/rnn

证书

　　MIT